Machine Learning Specialization (Andrew Ng)
This playlist is my public study notebook while going through the Machine Learning Specialization, by Andrew Ng (DeepLearning.AI / Stanford), probably the most recommended course for anyone getting started in ML.
The idea here isn't just "solve the notebook and move on." Every lab in the course becomes a post where I retell what I understood, with an everyday-life metaphor, an example, and of course, the correct technical term, because you'll need it when you go looking for more on the topic later.
We start at the start: representing the simplest model there is, linear regression with one variable.
Playlists · 8 posts
- Optional Lab: Model Representation
The simplest ML model there is, f(x) = wx + b, taken apart: why a line, what w and b actually mean, and why 'finding the parameters by hand' doesn't scale.
- Optional Lab: the Cost Function
How to put a single number on how wrong a model is: J(w,b), why squared error, why divide by 2m, and why the cost surface is always a bowl.
- Optional Lab: Gradient Descent
The algorithm that walks itself down to the bottom of the cost bowl: the math behind gradient descent, what happens when the learning rate is too big, and why it stops on its own near the minimum.
- NumPy and Vectorization
Why every serious ML codebase uses NumPy instead of a Python list: vectors, matrices, the dot product, and the real (not the legendary) gap between a loop and a vectorized operation.
- Just a Little Extra: Loss Functions (MSE, MAE, Huber)
Why we squared the error back in post 2, and what happens if you choose differently: MAE, Huber, and the real impact a single outlier has on each choice.
- Feature Scaling and Learning Rate
Why the exact same gradient descent that worked fine so far breaks the moment one feature has a wildly different scale from the others, and how a basic statistics trick fixes it for good.
- Feature Engineering and Polynomial Regression
How to make the exact same linear regression fit curves, by engineering new features instead of switching algorithms, and why that forced me to finally confront overfitting head-on.
- Linear Regression with Scikit-Learn
After building gradient descent, normalization, and feature engineering by hand, I finally use scikit-learn, and find out the course's 'default' model hides two surprises nobody warns you about.